{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Gentle Introduction to use Isolation Forest for Anomaly Detection\n",
    "#### Author Nagdev Amruthnath\n",
    "Date: 1/9/2019\n",
    "\n",
    "#### Citation Info\n",
    "If you are using this for your research, please use the following for citation.\n",
    "\n",
    "Amruthnath, Nagdev, and Tarun Gupta. \"A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance.\" In 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), pp. 355-361. IEEE, 2018.\n",
    "\n",
    "#### Disclaimer\n",
    "This is a tutorial for performing fault detection using machine learning. You this code at your own risk. I do not gurantee that this would work as shown below. If you have any suggestions please branch this project.\n",
    "\n",
    "### Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(warn=-1)\n",
    "\n",
    "# load libraries\n",
    "library(dplyr)\n",
    "library(h2o)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Load data\n",
    "Here we are using data from a bench press. There are total of four different states in this machine and they are split into four different csv files. We need to load the data first. In the data time represents the time between samples, ax is the acceleration on x axis, ay is the acceleration on y axis, az is the acceleration on z axis and at is the G's. The data was collected at sample rate of 100hz.\n",
    "\n",
    "Four different states of the machine were collected\n",
    "\n",
    "    1. Nothing attached to drill press\n",
    "    2. Wooden base attached to drill press\n",
    "    3. Imbalance created by adding weight to one end of wooden base\n",
    "    4. Imbalacne created by adding weight to two ends of wooden base."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>time</th><th scope=col>ax</th><th scope=col>ay</th><th scope=col>az</th><th scope=col>aT</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.002</td><td>-0.3246</td><td> 0.2748</td><td> 0.1502</td><td>0.451</td></tr>\n",
       "\t<tr><td>0.009</td><td> 0.6020</td><td>-0.1900</td><td>-0.3227</td><td>0.709</td></tr>\n",
       "\t<tr><td>0.019</td><td> 0.9787</td><td> 0.3258</td><td> 0.0124</td><td>1.032</td></tr>\n",
       "\t<tr><td>0.027</td><td> 0.6141</td><td>-0.4179</td><td> 0.0471</td><td>0.744</td></tr>\n",
       "\t<tr><td>0.038</td><td>-0.3218</td><td>-0.6389</td><td>-0.4259</td><td>0.833</td></tr>\n",
       "\t<tr><td>0.047</td><td>-0.3607</td><td> 0.1332</td><td>-0.1291</td><td>0.406</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       " time & ax & ay & az & aT\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.002 & -0.3246 &  0.2748 &  0.1502 & 0.451\\\\\n",
       "\t 0.009 &  0.6020 & -0.1900 & -0.3227 & 0.709\\\\\n",
       "\t 0.019 &  0.9787 &  0.3258 &  0.0124 & 1.032\\\\\n",
       "\t 0.027 &  0.6141 & -0.4179 &  0.0471 & 0.744\\\\\n",
       "\t 0.038 & -0.3218 & -0.6389 & -0.4259 & 0.833\\\\\n",
       "\t 0.047 & -0.3607 &  0.1332 & -0.1291 & 0.406\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| time &lt;dbl&gt; | ax &lt;dbl&gt; | ay &lt;dbl&gt; | az &lt;dbl&gt; | aT &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 0.002 | -0.3246 |  0.2748 |  0.1502 | 0.451 |\n",
       "| 0.009 |  0.6020 | -0.1900 | -0.3227 | 0.709 |\n",
       "| 0.019 |  0.9787 |  0.3258 |  0.0124 | 1.032 |\n",
       "| 0.027 |  0.6141 | -0.4179 |  0.0471 | 0.744 |\n",
       "| 0.038 | -0.3218 | -0.6389 | -0.4259 | 0.833 |\n",
       "| 0.047 | -0.3607 |  0.1332 | -0.1291 | 0.406 |\n",
       "\n"
      ],
      "text/plain": [
       "  time  ax      ay      az      aT   \n",
       "1 0.002 -0.3246  0.2748  0.1502 0.451\n",
       "2 0.009  0.6020 -0.1900 -0.3227 0.709\n",
       "3 0.019  0.9787  0.3258  0.0124 1.032\n",
       "4 0.027  0.6141 -0.4179  0.0471 0.744\n",
       "5 0.038 -0.3218 -0.6389 -0.4259 0.833\n",
       "6 0.047 -0.3607  0.1332 -0.1291 0.406"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setwd(\"/home/\")\n",
    "#read csv files\n",
    "file1 = read.csv(\"dry run.csv\", sep=\",\", header =T)\n",
    "file2 = read.csv(\"base.csv\", sep=\",\", header =T)\n",
    "file3 = read.csv(\"imbalance 1.csv\", sep=\",\", header =T)\n",
    "file4 = read.csv(\"imbalance 2.csv\", sep=\",\", header =T)\n",
    "head(file1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the summary of each file using summary function in R. Below, we can observe that 66 seconds long data is available. We also have min, max and mean for each of the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      time               ax                  ay                  az          \n",
       " Min.   :  0.004   Min.   :-1.402700   Min.   :-1.693300   Min.   :-3.18930  \n",
       " 1st Qu.: 27.005   1st Qu.:-0.311100   1st Qu.:-0.429600   1st Qu.:-0.57337  \n",
       " Median : 54.142   Median : 0.015100   Median :-0.010700   Median :-0.11835  \n",
       " Mean   : 54.086   Mean   : 0.005385   Mean   :-0.002534   Mean   :-0.09105  \n",
       " 3rd Qu.: 81.146   3rd Qu.: 0.314800   3rd Qu.: 0.419475   3rd Qu.: 0.34815  \n",
       " Max.   :108.127   Max.   : 1.771900   Max.   : 1.515600   Max.   : 5.04610  \n",
       "       aT        \n",
       " Min.   :0.0360  \n",
       " 1st Qu.:0.6270  \n",
       " Median :0.8670  \n",
       " Mean   :0.9261  \n",
       " 3rd Qu.:1.1550  \n",
       " Max.   :5.2950  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summary of each file\n",
    "summary(file2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Aggregration and feature extraction\n",
    "Here, the data is aggregated by 1 minute and features are extracted. Features are extracted to reduce the dimension of the data and only storing the representation of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 6 × 21</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>group</th><th scope=col>ax_mean</th><th scope=col>ax_sd</th><th scope=col>ax_min</th><th scope=col>ax_max</th><th scope=col>ax_median</th><th scope=col>ay_mean</th><th scope=col>ay_sd</th><th scope=col>ay_min</th><th scope=col>ay_may</th><th scope=col>⋯</th><th scope=col>az_mean</th><th scope=col>az_sd</th><th scope=col>az_min</th><th scope=col>az_maz</th><th scope=col>az_median</th><th scope=col>aT_mean</th><th scope=col>aT_sd</th><th scope=col>aT_min</th><th scope=col>aT_maT</th><th scope=col>aT_median</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>-0.038164706</td><td>0.6594686</td><td>-1.2587</td><td>1.3821</td><td>-0.0955</td><td>-0.0682627451</td><td>0.7506785</td><td>-1.3892</td><td>1.6418</td><td>⋯</td><td>-0.13803333</td><td>0.9845115</td><td>-2.6753</td><td>2.7507</td><td> 0.0254</td><td>1.273216</td><td>0.5830149</td><td>0.400</td><td>3.029</td><td>1.0770</td></tr>\n",
       "\t<tr><td>1</td><td>-0.005806122</td><td>0.6325808</td><td>-1.6194</td><td>1.1943</td><td>-0.0015</td><td> 0.0037908163</td><td>0.7819044</td><td>-1.5625</td><td>1.5428</td><td>⋯</td><td>-0.20496837</td><td>0.9252188</td><td>-3.0774</td><td>2.7158</td><td>-0.2121</td><td>1.263622</td><td>0.5448447</td><td>0.410</td><td>3.197</td><td>1.1375</td></tr>\n",
       "\t<tr><td>2</td><td> 0.069845455</td><td>0.6665500</td><td>-1.4554</td><td>1.4667</td><td> 0.1070</td><td> 0.0744333333</td><td>0.8022922</td><td>-1.4800</td><td>1.7951</td><td>⋯</td><td>-0.06405354</td><td>0.9293866</td><td>-1.8205</td><td>2.4862</td><td>-0.1512</td><td>1.298364</td><td>0.5131552</td><td>0.255</td><td>2.644</td><td>1.2830</td></tr>\n",
       "\t<tr><td>3</td><td> 0.011552525</td><td>0.5511310</td><td>-1.9254</td><td>1.2034</td><td> 0.0675</td><td> 0.0008262626</td><td>0.7894209</td><td>-2.0042</td><td>1.5577</td><td>⋯</td><td>-0.09287879</td><td>0.8893505</td><td>-2.1562</td><td>3.2355</td><td>-0.1672</td><td>1.203848</td><td>0.5125826</td><td>0.393</td><td>3.322</td><td>1.1180</td></tr>\n",
       "\t<tr><td>4</td><td> 0.046688119</td><td>0.6426574</td><td>-1.7805</td><td>1.4837</td><td> 0.0836</td><td>-0.0177594059</td><td>0.7510811</td><td>-1.6629</td><td>1.4369</td><td>⋯</td><td>-0.13990000</td><td>0.9265720</td><td>-1.8515</td><td>3.5451</td><td>-0.1741</td><td>1.226267</td><td>0.5824608</td><td>0.313</td><td>3.597</td><td>1.1720</td></tr>\n",
       "\t<tr><td>5</td><td> 0.006678788</td><td>0.5780957</td><td>-1.4719</td><td>1.4355</td><td> 0.0536</td><td> 0.0013626263</td><td>0.7812245</td><td>-1.6293</td><td>1.6362</td><td>⋯</td><td>-0.16540000</td><td>0.9091516</td><td>-2.5561</td><td>2.9196</td><td>-0.2588</td><td>1.209515</td><td>0.5664847</td><td>0.336</td><td>3.035</td><td>1.1590</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 21\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       " group & ax\\_mean & ax\\_sd & ax\\_min & ax\\_max & ax\\_median & ay\\_mean & ay\\_sd & ay\\_min & ay\\_may & ay\\_median & az\\_mean & az\\_sd & az\\_min & az\\_maz & az\\_median & aT\\_mean & aT\\_sd & aT\\_min & aT\\_maT & aT\\_median\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0 & -0.038164706 & 0.6594686 & -1.2587 & 1.3821 & -0.0955 & -0.0682627451 & 0.7506785 & -1.3892 & 1.6418 & -0.19000 & -0.13803333 & 0.9845115 & -2.6753 & 2.7507 &  0.0254 & 1.273216 & 0.5830149 & 0.400 & 3.029 & 1.0770\\\\\n",
       "\t 1 & -0.005806122 & 0.6325808 & -1.6194 & 1.1943 & -0.0015 &  0.0037908163 & 0.7819044 & -1.5625 & 1.5428 &  0.01005 & -0.20496837 & 0.9252188 & -3.0774 & 2.7158 & -0.2121 & 1.263622 & 0.5448447 & 0.410 & 3.197 & 1.1375\\\\\n",
       "\t 2 &  0.069845455 & 0.6665500 & -1.4554 & 1.4667 &  0.1070 &  0.0744333333 & 0.8022922 & -1.4800 & 1.7951 &  0.11860 & -0.06405354 & 0.9293866 & -1.8205 & 2.4862 & -0.1512 & 1.298364 & 0.5131552 & 0.255 & 2.644 & 1.2830\\\\\n",
       "\t 3 &  0.011552525 & 0.5511310 & -1.9254 & 1.2034 &  0.0675 &  0.0008262626 & 0.7894209 & -2.0042 & 1.5577 & -0.00270 & -0.09287879 & 0.8893505 & -2.1562 & 3.2355 & -0.1672 & 1.203848 & 0.5125826 & 0.393 & 3.322 & 1.1180\\\\\n",
       "\t 4 &  0.046688119 & 0.6426574 & -1.7805 & 1.4837 &  0.0836 & -0.0177594059 & 0.7510811 & -1.6629 & 1.4369 & -0.02530 & -0.13990000 & 0.9265720 & -1.8515 & 3.5451 & -0.1741 & 1.226267 & 0.5824608 & 0.313 & 3.597 & 1.1720\\\\\n",
       "\t 5 &  0.006678788 & 0.5780957 & -1.4719 & 1.4355 &  0.0536 &  0.0013626263 & 0.7812245 & -1.6293 & 1.6362 & -0.09910 & -0.16540000 & 0.9091516 & -2.5561 & 2.9196 & -0.2588 & 1.209515 & 0.5664847 & 0.336 & 3.035 & 1.1590\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 21\n",
       "\n",
       "| group &lt;fct&gt; | ax_mean &lt;dbl&gt; | ax_sd &lt;dbl&gt; | ax_min &lt;dbl&gt; | ax_max &lt;dbl&gt; | ax_median &lt;dbl&gt; | ay_mean &lt;dbl&gt; | ay_sd &lt;dbl&gt; | ay_min &lt;dbl&gt; | ay_may &lt;dbl&gt; | ⋯ ⋯ | az_mean &lt;dbl&gt; | az_sd &lt;dbl&gt; | az_min &lt;dbl&gt; | az_maz &lt;dbl&gt; | az_median &lt;dbl&gt; | aT_mean &lt;dbl&gt; | aT_sd &lt;dbl&gt; | aT_min &lt;dbl&gt; | aT_maT &lt;dbl&gt; | aT_median &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0 | -0.038164706 | 0.6594686 | -1.2587 | 1.3821 | -0.0955 | -0.0682627451 | 0.7506785 | -1.3892 | 1.6418 | ⋯ | -0.13803333 | 0.9845115 | -2.6753 | 2.7507 |  0.0254 | 1.273216 | 0.5830149 | 0.400 | 3.029 | 1.0770 |\n",
       "| 1 | -0.005806122 | 0.6325808 | -1.6194 | 1.1943 | -0.0015 |  0.0037908163 | 0.7819044 | -1.5625 | 1.5428 | ⋯ | -0.20496837 | 0.9252188 | -3.0774 | 2.7158 | -0.2121 | 1.263622 | 0.5448447 | 0.410 | 3.197 | 1.1375 |\n",
       "| 2 |  0.069845455 | 0.6665500 | -1.4554 | 1.4667 |  0.1070 |  0.0744333333 | 0.8022922 | -1.4800 | 1.7951 | ⋯ | -0.06405354 | 0.9293866 | -1.8205 | 2.4862 | -0.1512 | 1.298364 | 0.5131552 | 0.255 | 2.644 | 1.2830 |\n",
       "| 3 |  0.011552525 | 0.5511310 | -1.9254 | 1.2034 |  0.0675 |  0.0008262626 | 0.7894209 | -2.0042 | 1.5577 | ⋯ | -0.09287879 | 0.8893505 | -2.1562 | 3.2355 | -0.1672 | 1.203848 | 0.5125826 | 0.393 | 3.322 | 1.1180 |\n",
       "| 4 |  0.046688119 | 0.6426574 | -1.7805 | 1.4837 |  0.0836 | -0.0177594059 | 0.7510811 | -1.6629 | 1.4369 | ⋯ | -0.13990000 | 0.9265720 | -1.8515 | 3.5451 | -0.1741 | 1.226267 | 0.5824608 | 0.313 | 3.597 | 1.1720 |\n",
       "| 5 |  0.006678788 | 0.5780957 | -1.4719 | 1.4355 |  0.0536 |  0.0013626263 | 0.7812245 | -1.6293 | 1.6362 | ⋯ | -0.16540000 | 0.9091516 | -2.5561 | 2.9196 | -0.2588 | 1.209515 | 0.5664847 | 0.336 | 3.035 | 1.1590 |\n",
       "\n"
      ],
      "text/plain": [
       "  group ax_mean      ax_sd     ax_min  ax_max ax_median ay_mean       ay_sd    \n",
       "1 0     -0.038164706 0.6594686 -1.2587 1.3821 -0.0955   -0.0682627451 0.7506785\n",
       "2 1     -0.005806122 0.6325808 -1.6194 1.1943 -0.0015    0.0037908163 0.7819044\n",
       "3 2      0.069845455 0.6665500 -1.4554 1.4667  0.1070    0.0744333333 0.8022922\n",
       "4 3      0.011552525 0.5511310 -1.9254 1.2034  0.0675    0.0008262626 0.7894209\n",
       "5 4      0.046688119 0.6426574 -1.7805 1.4837  0.0836   -0.0177594059 0.7510811\n",
       "6 5      0.006678788 0.5780957 -1.4719 1.4355  0.0536    0.0013626263 0.7812245\n",
       "  ay_min  ay_may ⋯ az_mean     az_sd     az_min  az_maz az_median aT_mean \n",
       "1 -1.3892 1.6418 ⋯ -0.13803333 0.9845115 -2.6753 2.7507  0.0254   1.273216\n",
       "2 -1.5625 1.5428 ⋯ -0.20496837 0.9252188 -3.0774 2.7158 -0.2121   1.263622\n",
       "3 -1.4800 1.7951 ⋯ -0.06405354 0.9293866 -1.8205 2.4862 -0.1512   1.298364\n",
       "4 -2.0042 1.5577 ⋯ -0.09287879 0.8893505 -2.1562 3.2355 -0.1672   1.203848\n",
       "5 -1.6629 1.4369 ⋯ -0.13990000 0.9265720 -1.8515 3.5451 -0.1741   1.226267\n",
       "6 -1.6293 1.6362 ⋯ -0.16540000 0.9091516 -2.5561 2.9196 -0.2588   1.209515\n",
       "  aT_sd     aT_min aT_maT aT_median\n",
       "1 0.5830149 0.400  3.029  1.0770   \n",
       "2 0.5448447 0.410  3.197  1.1375   \n",
       "3 0.5131552 0.255  2.644  1.2830   \n",
       "4 0.5125826 0.393  3.322  1.1180   \n",
       "5 0.5824608 0.313  3.597  1.1720   \n",
       "6 0.5664847 0.336  3.035  1.1590   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file1$group = as.factor(round(file1$time))\n",
    "file2$group = as.factor(round(file2$time))\n",
    "file3$group = as.factor(round(file3$time))\n",
    "file4$group = as.factor(round(file4$time))\n",
    "#(file1,20)\n",
    "\n",
    "#list of all files\n",
    "files = list(file1, file2, file3, file4)\n",
    "\n",
    "#loop through all files and combine\n",
    "features = NULL\n",
    "for (i in 1:4){\n",
    "res = files[[i]] %>%\n",
    "    group_by(group) %>%\n",
    "    summarize(ax_mean = mean(ax),\n",
    "              ax_sd = sd(ax),\n",
    "              ax_min = min(ax),\n",
    "              ax_max = max(ax),\n",
    "              ax_median = median(ax),\n",
    "              ay_mean = mean(ay),\n",
    "              ay_sd = sd(ay),\n",
    "              ay_min = min(ay),\n",
    "              ay_may = max(ay),\n",
    "              ay_median = median(ay),\n",
    "              az_mean = mean(az),\n",
    "              az_sd = sd(az),\n",
    "              az_min = min(az),\n",
    "              az_maz = max(az),\n",
    "              az_median = median(az),\n",
    "              aT_mean = mean(aT),\n",
    "              aT_sd = sd(aT),\n",
    "              aT_min = min(aT),\n",
    "              aT_maT = max(aT),\n",
    "              aT_median = median(aT)\n",
    "             )\n",
    "    features = rbind(features, res)\n",
    "}\n",
    "\n",
    "#view all features\n",
    "head(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Train and Test Set\n",
    "To build an anomaly detection model, a train and test set is required. Here, the normal condition of the data is used for training and remaining is used for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create train and test set\n",
    "train = features[1:67,2:ncol(features)]\n",
    "test = features[68:nrow(features),2:ncol(features)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Isolation Forest\n",
    "Isolation Forest is similar in principle to Random Forest and is built on the basis of decision trees. Isolation Forest, however, identifies anomalies or outliers rather than profiling normal data points. Isolation Forest isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of that selected feature. This split depends on how long it takes to separate the points.\n",
    "\n",
    "Random partitioning produces noticeably shorter paths for anomalies. When a forest of random trees collectively produces shorter path lengths for particular samples, they are highly likely to be anomalies.\n",
    "\n",
    "The idea behind the Isolation Forest is as follows.\n",
    "\n",
    "We start by building multiple decision trees such that the trees isolate the observations in their leaves. Ideally, each leaf of the tree isolates exactly one observation from your data set. The trees are being split randomly. We assume that if one observation is similar to others in our data set, it will take more random splits to perfectly isolate this observation, as opposed to isolating an outlier.\n",
    "\n",
    "For an outlier that has some feature values significantly different from the other observations, randomly finding the split isolating it should not be too hard. As we build multiple isolation trees, hence the isolation forest, for each observation we can calculate the average number of splits across all the trees that isolate the observation. The average number of splits is then used as a score, where the less splits the observation needs, the more likely it is to be anomalous\n",
    " \n",
    "\n",
    "#### Isolation Forest using H2O package\n",
    "Use the h2o.init() method to initialize H2O. This method accepts the following options. Note that in most cases, simply using h2o.init() is all that a user is required to do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "H2O is not running yet, starting it now...\n",
      "\n",
      "Note:  In case of errors look at the following log files:\n",
      "    /tmp/RtmpF92fZW/h2o_UnknownUser_started_from_r.out\n",
      "    /tmp/RtmpF92fZW/h2o_UnknownUser_started_from_r.err\n",
      "\n",
      "\n",
      "Starting H2O JVM and connecting: . Connection successful!\n",
      "\n",
      "R is connected to the H2O cluster: \n",
      "    H2O cluster uptime:         1 seconds 646 milliseconds \n",
      "    H2O cluster timezone:       Etc/UTC \n",
      "    H2O data parsing timezone:  UTC \n",
      "    H2O cluster version:        3.26.0.2 \n",
      "    H2O cluster version age:    3 months and 17 days !!! \n",
      "    H2O cluster name:           H2O_started_from_R_root_wpw635 \n",
      "    H2O cluster total nodes:    1 \n",
      "    H2O cluster total memory:   29.00 GB \n",
      "    H2O cluster total cores:    1 \n",
      "    H2O cluster allowed cores:  1 \n",
      "    H2O cluster healthy:        TRUE \n",
      "    H2O Connection ip:          localhost \n",
      "    H2O Connection port:        54321 \n",
      "    H2O Connection proxy:       NA \n",
      "    H2O Internal Security:      FALSE \n",
      "    H2O API Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4 \n",
      "    R Version:                  R version 3.3.3 (2017-03-06) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# initialize h2o cluser\n",
    "h2o.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The R object to be converted to an H2O object should be named so that it can be used in subsequent analysis. Also note that the R object is converted to a parsed H2O data object, and will be treated as a data frame by H2O in subsequent analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |======================================================================| 100%\n",
      "  |======================================================================| 100%\n"
     ]
    }
   ],
   "source": [
    "# convert train and test to h2o object\n",
    "train_h2o = as.h2o(train)\n",
    "test_h2o = as.h2o(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The h2o.isolationForest() function fits H2O's Isolation Forest models from within R. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |======================================================================| 100%\n"
     ]
    }
   ],
   "source": [
    "# build auto encoder model with 3 layers\n",
    "model_unsup = h2o.isolationForest( training_frame = train_h2o\n",
    "                                     , sample_rate = 0.1\n",
    "                                     , max_depth = 128\n",
    "                                     , ntrees = 1000\n",
    "                                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model Details:\n",
       "==============\n",
       "\n",
       "H2OAnomalyDetectionModel: isolationforest\n",
       "Model ID:  IsolationForest_model_R_1573655559531_1 \n",
       "Model Summary: \n",
       "  number_of_trees number_of_internal_trees model_size_in_bytes min_depth\n",
       "1            1000                     1000              149603         1\n",
       "  max_depth mean_depth min_leaves max_leaves mean_leaves\n",
       "1        10    4.55200          2         16     7.19300\n",
       "\n",
       "\n",
       "H2OAnomalyDetectionMetrics: isolationforest\n",
       "** Reported on training data. **\n",
       "** Metrics reported on Out-Of-Bag training samples **\n",
       "\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# view the model\n",
    "model_unsup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detect anomalies in an H2O dataset using an H2O isolation forst model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |======================================================================| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>predict</th><th scope=col>mean_length</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.6838879</td><td>1.913</td></tr>\n",
       "\t<tr><td>0.3029772</td><td>2.348</td></tr>\n",
       "\t<tr><td>0.2267951</td><td>2.435</td></tr>\n",
       "\t<tr><td>0.3826620</td><td>2.257</td></tr>\n",
       "\t<tr><td>0.2688266</td><td>2.387</td></tr>\n",
       "\t<tr><td>0.2040280</td><td>2.461</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " predict & mean\\_length\\\\\n",
       " <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.6838879 & 1.913\\\\\n",
       "\t 0.3029772 & 2.348\\\\\n",
       "\t 0.2267951 & 2.435\\\\\n",
       "\t 0.3826620 & 2.257\\\\\n",
       "\t 0.2688266 & 2.387\\\\\n",
       "\t 0.2040280 & 2.461\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| predict &lt;dbl&gt; | mean_length &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 0.6838879 | 1.913 |\n",
       "| 0.3029772 | 2.348 |\n",
       "| 0.2267951 | 2.435 |\n",
       "| 0.3826620 | 2.257 |\n",
       "| 0.2688266 | 2.387 |\n",
       "| 0.2040280 | 2.461 |\n",
       "\n"
      ],
      "text/plain": [
       "  predict   mean_length\n",
       "1 0.6838879 1.913      \n",
       "2 0.3029772 2.348      \n",
       "3 0.2267951 2.435      \n",
       "4 0.3826620 2.257      \n",
       "5 0.2688266 2.387      \n",
       "6 0.2040280 2.461      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_score = h2o.predict( model_unsup, train_h2o ) %>% as.data.frame()\n",
    "head(train_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calulate the threshold value for train anomaly scores. Various methods can be used such as calculating the quantiles, max, median, min etc. It all depends on the use case. Here we will use quantile with probability of 99.9%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have anomaly score for train and its thresholds, we can predict the new anomaly scores for test data and plot it to see how it differs from train data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>90%:</strong> 0.517162872154116"
      ],
      "text/latex": [
       "\\textbf{90\\textbackslash{}\\%:} 0.517162872154116"
      ],
      "text/markdown": [
       "**90%:** 0.517162872154116"
      ],
      "text/plain": [
       "      90% \n",
       "0.5171629 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>predict</th><th scope=col>mean_length</th><th scope=col>anomaly</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.6838879</td><td>1.913</td><td>1</td></tr>\n",
       "\t<tr><td>0.3029772</td><td>2.348</td><td>0</td></tr>\n",
       "\t<tr><td>0.2267951</td><td>2.435</td><td>0</td></tr>\n",
       "\t<tr><td>0.3826620</td><td>2.257</td><td>0</td></tr>\n",
       "\t<tr><td>0.2688266</td><td>2.387</td><td>0</td></tr>\n",
       "\t<tr><td>0.2040280</td><td>2.461</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       " predict & mean\\_length & anomaly\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.6838879 & 1.913 & 1\\\\\n",
       "\t 0.3029772 & 2.348 & 0\\\\\n",
       "\t 0.2267951 & 2.435 & 0\\\\\n",
       "\t 0.3826620 & 2.257 & 0\\\\\n",
       "\t 0.2688266 & 2.387 & 0\\\\\n",
       "\t 0.2040280 & 2.461 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| predict &lt;dbl&gt; | mean_length &lt;dbl&gt; | anomaly &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 0.6838879 | 1.913 | 1 |\n",
       "| 0.3029772 | 2.348 | 0 |\n",
       "| 0.2267951 | 2.435 | 0 |\n",
       "| 0.3826620 | 2.257 | 0 |\n",
       "| 0.2688266 | 2.387 | 0 |\n",
       "| 0.2040280 | 2.461 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "  predict   mean_length anomaly\n",
       "1 0.6838879 1.913       1      \n",
       "2 0.3029772 2.348       0      \n",
       "3 0.2267951 2.435       0      \n",
       "4 0.3826620 2.257       0      \n",
       "5 0.2688266 2.387       0      \n",
       "6 0.2040280 2.461       0      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate thresholds from train data\n",
    "threshold = quantile(train_score$predict, probs = 0.90)\n",
    "threshold\n",
    "train_score$anomaly = ifelse(train_score$predict>threshold, 1, 0)\n",
    "\n",
    "head(train_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  |======================================================================| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>predict</th><th scope=col>mean_length</th><th scope=col>anomaly</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.8528897</td><td>1.720</td><td>1</td></tr>\n",
       "\t<tr><td>0.7819615</td><td>1.801</td><td>1</td></tr>\n",
       "\t<tr><td>0.8056042</td><td>1.774</td><td>1</td></tr>\n",
       "\t<tr><td>0.8879159</td><td>1.680</td><td>1</td></tr>\n",
       "\t<tr><td>0.8169877</td><td>1.761</td><td>1</td></tr>\n",
       "\t<tr><td>0.8187391</td><td>1.759</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       " predict & mean\\_length & anomaly\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.8528897 & 1.720 & 1\\\\\n",
       "\t 0.7819615 & 1.801 & 1\\\\\n",
       "\t 0.8056042 & 1.774 & 1\\\\\n",
       "\t 0.8879159 & 1.680 & 1\\\\\n",
       "\t 0.8169877 & 1.761 & 1\\\\\n",
       "\t 0.8187391 & 1.759 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| predict &lt;dbl&gt; | mean_length &lt;dbl&gt; | anomaly &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 0.8528897 | 1.720 | 1 |\n",
       "| 0.7819615 | 1.801 | 1 |\n",
       "| 0.8056042 | 1.774 | 1 |\n",
       "| 0.8879159 | 1.680 | 1 |\n",
       "| 0.8169877 | 1.761 | 1 |\n",
       "| 0.8187391 | 1.759 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  predict   mean_length anomaly\n",
       "1 0.8528897 1.720       1      \n",
       "2 0.7819615 1.801       1      \n",
       "3 0.8056042 1.774       1      \n",
       "4 0.8879159 1.680       1      \n",
       "5 0.8169877 1.761       1      \n",
       "6 0.8187391 1.759       1      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_score = h2o.predict( model_unsup, test_h2o ) %>% as.data.frame()\n",
    "\n",
    "test_score$anomaly = ifelse(test_score$predict>threshold, 1, 0)\n",
    "\n",
    "head(test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>predict</th><th scope=col>mean_length</th><th scope=col>anomaly</th><th scope=col>threshold</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.6838879</td><td>1.913</td><td>1</td><td>0.5171629</td></tr>\n",
       "\t<tr><td>0.3029772</td><td>2.348</td><td>0</td><td>0.5171629</td></tr>\n",
       "\t<tr><td>0.2267951</td><td>2.435</td><td>0</td><td>0.5171629</td></tr>\n",
       "\t<tr><td>0.3826620</td><td>2.257</td><td>0</td><td>0.5171629</td></tr>\n",
       "\t<tr><td>0.2688266</td><td>2.387</td><td>0</td><td>0.5171629</td></tr>\n",
       "\t<tr><td>0.2040280</td><td>2.461</td><td>0</td><td>0.5171629</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " predict & mean\\_length & anomaly & threshold\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.6838879 & 1.913 & 1 & 0.5171629\\\\\n",
       "\t 0.3029772 & 2.348 & 0 & 0.5171629\\\\\n",
       "\t 0.2267951 & 2.435 & 0 & 0.5171629\\\\\n",
       "\t 0.3826620 & 2.257 & 0 & 0.5171629\\\\\n",
       "\t 0.2688266 & 2.387 & 0 & 0.5171629\\\\\n",
       "\t 0.2040280 & 2.461 & 0 & 0.5171629\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| predict &lt;dbl&gt; | mean_length &lt;dbl&gt; | anomaly &lt;dbl&gt; | threshold &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 0.6838879 | 1.913 | 1 | 0.5171629 |\n",
       "| 0.3029772 | 2.348 | 0 | 0.5171629 |\n",
       "| 0.2267951 | 2.435 | 0 | 0.5171629 |\n",
       "| 0.3826620 | 2.257 | 0 | 0.5171629 |\n",
       "| 0.2688266 | 2.387 | 0 | 0.5171629 |\n",
       "| 0.2040280 | 2.461 | 0 | 0.5171629 |\n",
       "\n"
      ],
      "text/plain": [
       "  predict   mean_length anomaly threshold\n",
       "1 0.6838879 1.913       1       0.5171629\n",
       "2 0.3029772 2.348       0       0.5171629\n",
       "3 0.2267951 2.435       0       0.5171629\n",
       "4 0.3826620 2.257       0       0.5171629\n",
       "5 0.2688266 2.387       0       0.5171629\n",
       "6 0.2040280 2.461       0       0.5171629"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combine the train and test anomaly scores for visulaizatio\n",
    "results = data.frame(rbind(train_score,test_score), threshold)\n",
    "head(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are plotted below. The x axis is the observations and y axis is the anomaly score. The green points are the trained data and red are test data. We can note that most of the data that was trained lied below the anomaly limit. Its also interesting to note the increasing trend pattern for the anomaly scores for other state of the machine. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X3PX4pkOHrIbi4nT0qJo1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btvV95e7qv6AmP6i2trZNmzapsz169Fi9evXi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\n5/x/08Cd7lCoQGEAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Anomaly Detection Results”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjust plot sizes\n",
    "options(repr.plot.width = 15, repr.plot.height = 6)\n",
    "plot(results$predict, type = 'n', xlab='observations', ylab='Predicted Score', main = \"Anomaly Detection Results\")\n",
    "points(results$predict, pch=19, col=ifelse(results$predict < threshold, \"green\", \"red\"))\n",
    "abline(h=threshold, col='red', lwd=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need to close H2O cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.shutdown(prompt=F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session info\n",
    "Below is the session info for the the packages and their versions used in this analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.3.3 (2017-03-06)\n",
       "Platform: x86_64-pc-linux-gnu (64-bit)\n",
       "Running under: Debian GNU/Linux 9 (stretch)\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       \n",
       " [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   \n",
       " [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          \n",
       "[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   \n",
       "\n",
       "attached base packages:\n",
       "[1] stats     graphics  grDevices utils     datasets  methods   base     \n",
       "\n",
       "other attached packages:\n",
       "[1] h2o_3.26.0.2 dplyr_0.8.3 \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] Rcpp_1.0.2          magrittr_1.5        tidyselect_0.2.5   \n",
       " [4] uuid_0.1-2          R6_2.4.0            rlang_0.4.1        \n",
       " [7] tools_3.3.3         htmltools_0.4.0     assertthat_0.2.1   \n",
       "[10] digest_0.6.22       tibble_2.1.3        crayon_1.3.4       \n",
       "[13] IRdisplay_0.7.0     purrr_0.3.3         repr_1.0.1         \n",
       "[16] base64enc_0.1-3     vctrs_0.2.0         bitops_1.0-6       \n",
       "[19] RCurl_1.95-4.12     IRkernel_1.0.2.9000 zeallot_0.1.0      \n",
       "[22] glue_1.3.1          evaluate_0.14       pbdZMQ_0.3-3       \n",
       "[25] pillar_1.4.2        backports_1.1.5     jsonlite_1.6       \n",
       "[28] pkgconfig_2.0.3    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References  \n",
    "\n",
    "[1] http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/if.html   \n",
    "[2] https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf   \n",
    "[3] https://github.com/h2oai/h2o-tutorials/blob/master/tutorials/isolation-forest/interpreting_isolation-forest.ipynb   "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
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  },
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   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
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